A novel feature extraction method for ship-radiated noise

Yang, Huirong; Li, Lulu; Li, Guohui; Guan, Qian-ru

doi:10.1016/j.dt.2021.03.012

Cited by 43 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It decomposes the multi-modulation signal into a few intrinsic mode functions (IMFS), and each IMF is considered as a mono-component. Once born, EMD is widely used in the fields of mechanical failure diagnosis, biomedical science, and underwater acoustic signal analysis [ 8 , 9 , 10 , 11 , 12 , 13 ]. However, the result of EMD is highly dependent on the local extremum searching algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

Yang

Zhou

et al. 2023

Entropy

View full text Add to dashboard Cite

As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

Yang

Zhou

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…The first is the lack of strict mathematical theoretical basis ( Li et al, 2018 ). The second is the existence of modal aliasing ( Jiang et al, 2019 , Lang et al, 2020 , Yang et al, 2021 ). It often separates similar frequencies into multiple patterns, which can lead to significant error in the prediction.…”

Section: Introductionmentioning

confidence: 99%

A new hybrid prediction model of cumulative COVID-19 confirmed data

Chen

Yang

2022

Process Safety and Environmental Protection

Self Cite

View full text Add to dashboard Cite

“…Ship-radiated noise signal (S-S) as a kind of underwater acoustic signal (Li et al, 2019;Zhang et al, 2020;Esmaiel et al, 2022), which can indicate the physical characteristics of ships (Wang et al, 2017;Yang et al, 2022). The extraction of nonlinear dynamic indexes from S-Ss are helpful to the classification and recognition of different ships (Bao et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Multi-scale permutation Lempel-Ziv complexity and its application in feature extraction for Ship-radiated noise

2022

Front. Mar. Sci.

View full text Add to dashboard Cite

Permutation Lempel-Ziv complexity (PLZC) is a recently proposed method for analyzing signal complexity. However, PLZC only characterizes the signal complexity from single scale and has certain limitations. In order to overcome these shortcomings and improve the performance of feature extraction for underwater acoustic signal, this paper introduced coarse graining operation, proposed the multi-scale permutation Lempel-Ziv complexity (MPLZC), and proposed an automatic hybrid multi-feature extraction method for ship-radiated noise signal (S-S) based on multi-scale Lempel-Ziv complexity (MLZC), multi-scale permutation entropy (MPE) and MPLZC. The results of simulation and realistic experiments show that MPLZC can better reflect the change of signal complexity in detecting the dynamic change of signals, and more effectively distinguish white noise, pink noise and blue noise than MPE and MLZC; compared with the three feature extraction methods based on MLZC, MPE and MPLZC respectively, the proposed method has the highest recognition rates of six S-Ss under the same number of features, and the recognition rate reaches 100% when the number of features is 5; the introduction of MPLZC significantly improves the performance for ship-radiated noise signal of the automatic hybrid multi-feature extraction method. It is indicated that the proposed method, as a new underwater acoustic technology, is valid in other underwater acoustic signals.

show abstract

A novel feature extraction method for ship-radiated noise

Cited by 43 publications

References 35 publications

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

A new hybrid prediction model of cumulative COVID-19 confirmed data

Multi-scale permutation Lempel-Ziv complexity and its application in feature extraction for Ship-radiated noise

Contact Info

Product

Resources

About